DiscreteRate
Server Details
Run DRS demos (Fast-Slow Drain, Hamburger Duo, Valdez Tanker) and explore the paradigm.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
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Tool Definition Quality
Average 4.3/5 across 14 of 14 tools scored. Lowest: 3.2/5.
Each tool has a distinct purpose: list, describe, explain, and run demos. The explain_* tools cover separate concepts, run_* tools target specific demos with unique outputs, and run_showcase is the only interactive compute tool, preventing ambiguity.
All tools follow a strictly consistent verb_noun snake_case pattern: list_drs_demos, describe_demo, explain_*, run_*. There is no mixing of conventions, making the API predictable.
With 14 tools covering listing, detailed descriptions, conceptual explanations, and demo execution (including a customizable run), the number is well-scoped for a simulation education server, avoiding both bloat and insufficiency.
The tool set provides full lifecycle coverage for the domain: discover (list), understand (describe + explain), and execute (run demos + live experiments). No obvious gaps such as version queries or model comparisons are needed for the stated educational purpose.
Available Tools
14 toolsdescribe_demoARead-onlyIdempotentInspect
Full per-demo write-up: history, what it teaches, what to expect from the run_* output. Use this to ground the user before triggering a sim run, or to explain WHY the demo exists when the user asks a conceptual question about it.
| Name | Required | Description | Default |
|---|---|---|---|
| demo | Yes | Which DRS demo to act on. See list_drs_demos for the catalog. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds context that the tool produces a write-up covering history, teachings, and expected output, which is valuable beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first introduces purpose and content, second gives usage guidance. No wasted words, front-loaded with essential info.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 1-parameter tool with good annotations and no output schema, the description fully explains the tool's purpose, usage context, and output content. It is complete enough for an agent to differentiate from sibling tools and decide when to invoke.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Parameter 'demo' has schema description and enum values. Description adds meaning by explaining what the output for each demo will contain (history, teachings, expected output). With 100% schema coverage, baseline is 3; added context justifies a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Full per-demo write-up: history, what it teaches, what to expect from the run_* output.' It specifies the verb (describe/explain) and resource (one demo), and distinguishes from sibling run_* tools and list_drs_demos.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description explicitly says 'Use this to ground the user before triggering a sim run, or to explain WHY the demo exists when the user asks a conceptual question about it.' This provides clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_des_vs_drs_event_complexityARead-onlyIdempotentInspect
Return a focused write-up of the event-count complexity differences between DES and DRS, with the worked Fast-Slow Drain numbers (Continuous ~thousands vs DES ~500 vs DRS 10 events for the same 100-minute model). Use this when the user wants the practitioner-visible payoff of DRS — the 50× event-count reduction at the boundary-transition layer. Deterministic text.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds 'Deterministic text,' which aligns with idempotentHint and clarifies output nature. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, zero waste. Every sentence earns its place: first states what it returns, second gives usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description fully informs agents about purpose and usage. No missing information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters; schema coverage is 100% trivially. Description adequately compensates by explaining functionality without needing param details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns a focused write-up on DES vs DRS event-count complexity with specific numbers. It distinguishes from siblings like explain_discrete_rate_simulation by focusing on the comparison and the practical payoff.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this when the user wants the practitioner-visible payoff of DRS — the 50× event-count reduction at the boundary-transition layer.' It lacks explicit when-not-to-use guidelines, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_discrete_rate_simulationARead-onlyIdempotentInspect
Return a textbook-tier explainer of Discrete Rate Simulation: how it differs from DES and CT, the three primitives (Constraint / Buffer / Interrupt), paradigm integration via F2I / I2F. Use this for 'what is DRS?' / 'how is this different from DES?' / 'where does DRS fit in the simulation landscape?' style questions. Deterministic text — no engine call, no RNG.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, destructiveHint=false. Description adds that it produces deterministic text without engine calls or RNG, which goes beyond the annotations to clarify the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, each serving a distinct purpose: one to state what the tool returns, another to specify usage context and behavior. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description fully covers what the tool does, when to use it, and its deterministic nature. It is complete for its intended purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so schema coverage is 100% and baseline is 4. The description does not need to add parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states it returns a 'textbook-tier explainer' of Discrete Rate Simulation, listing specific topics (primitive, parity) and targeted questions, clearly distinguishing from sibling tools like explain_des_vs_drs_event_complexity or explain_paradigm_integration.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: for 'what is DRS?', 'how is this different from DES?', 'where does DRS fit?' style questions, and clarifies it is 'Deterministic text — no engine call, no RNG', guiding the agent to use this for explanation rather than simulation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_paradigm_integrationARead-onlyIdempotentInspect
Return an explainer of paradigm integration — how DRS handles systems with both flows and items via F2I (Flow-to-Item) and I2F (Item-to-Flow) primitives. Use this when the user asks about Valdez-Tanker-style mixed-paradigm systems or 'how do flows and items coexist'. Deterministic text.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, indicating a safe, side-effect-free operation. The description adds 'Deterministic text', which provides additional behavioral clarity beyond annotations, confirming output stability.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load the purpose and usage. Every sentence adds value with no redundancy. Perfectly sized for quick agent comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description adequately explains the return type ('explainer', 'Deterministic text') and usage context. Combined with annotations, it gives agents all necessary information to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema coverage is 100% (empty). The description doesn't need to add parameter info; baseline 4 is appropriate for zero-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns an explainer of paradigm integration, specifically DRS handling flows and items via F2I and I2F primitives. This distinguishes it from sibling tools like explain_three_primitives, which focus on basic primitives, not integration.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this when the user asks about Valdez-Tanker-style mixed-paradigm systems or 'how do flows and items coexist'—providing clear context for when to invoke. However, it does not mention when not to use or list sibling alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_three_primitivesARead-onlyIdempotentInspect
Return a focused write-up of the three DRS modeling primitives: Constraint (rate-limiter), Buffer (accumulated state), Interrupt (stoppage). Use this when the user asks specifically about modeling primitives or how to spell a system in DRS. Deterministic text.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds 'Deterministic text' beyond what annotations provide (readOnlyHint, idempotentHint, destructiveHint). This informs the agent that the output is consistent across invocations, which is useful behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no wasted words. First sentence defines the tool's purpose, second sentence provides usage guidance and determinism. Perfectly front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple informational tool with no output schema, the description is fully adequate. It explains what is returned, when to use it, and its deterministic nature. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%. The description does not need to add parameter details. Baseline score of 4 is appropriate for 0-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it returns a focused write-up of the three DRS modeling primitives: Constraint, Buffer, Interrupt. It clearly identifies the specific resource and distinguishes its purpose from sibling tools like explain_des_vs_drs_event_complexity or explain_discrete_rate_simulation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes explicit guidance: 'Use this when the user asks specifically about modeling primitives or how to spell a system in DRS.' This clearly defines when to invoke, though it does not mention when not to use it or suggest alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_drs_demosARead-onlyIdempotentInspect
List the seven DRS demos (Fast-Slow Drain · Hamburger Duo · Valdez Tanker · Vegetable Plant · Chocolate Processing · Bottling Line SKU capacity · Tissue Line). Each is reproducible against the engine via the run_* tools. Use this to discover what's available before calling describe_demo or a run_* tool.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds context that the demos are reproducible via run_* tools, which is helpful but not extensive. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, compact, and front-loaded with the main action. Every sentence is useful, with no extraneous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, no output schema, and annotations covering safety/idempotency, the description is complete. It fully explains what the tool lists and when to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, and schema coverage is 100% trivially. Baseline for 0 parameters is 4; the description does not need to add parameter info.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool lists the seven DRS demos and enumerates them by name. It distinguishes itself from sibling tools like describe_demo and run_* tools by stating its purpose is to discover what is available before calling those.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use this tool: 'Use this to discover what's available before calling describe_demo or a run_* tool.' It does not explicitly state when not to use it, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_chocolate_processingARead-onlyInspect
Run the Chocolate Processing (CHOC) demo — Plant Builder's joint DES↔DRS bridge. Three systems in series (Bean Processing → Cocoa Powder → Chocolate): DES schedules campaigns and injects equipment failures, a DRS rate solver carries the continuous flow, a bridge couples them. Exercises all 7 controllers + Goal blocks. Returns the plant rollup (schedule occupancy vs busy utilization, total downtime, campaigns), per-product attainment, and per-system campaign timelines with downtime. ANTI-FABRICATION: numbers come from a real Plant Builder engine run; quote verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint and destructiveHint, so safety is clear. The description adds behavioral details: it runs a simulation, exercises controllers, returns specific data, and includes an anti-fabrication note. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative without being overly verbose. It is front-loaded with the main purpose and structured with details and a note on anti-fabrication. Slightly long but well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no inputs and no output schema, the description fully explains what the tool does and what it returns (plant rollup, attainment, timelines). No gaps remain for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so baseline is 4. The description does not need to clarify parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs the Chocolate Processing demo, a Plant Builder joint DES↔DRS bridge, and lists the three systems and outputs. This distinguishes it from sibling tools which are other demos or explanations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lacks explicit when-to-use or when-not-to-use guidance beyond describing the demo's purpose and output. It does not compare with sibling tools, though the context implies it is for chocolate processing scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_fast_slow_drainBRead-onlyInspect
Run the Fast-Slow Drain (FSD) demo — Damiron-Nastasi 2008 oscillating tank. The canonical DRS-vs-DES event-count demonstration. Returns engine output including the event counts (DES vs DRS), tank-level trace, and cycle summary. ANTI-FABRICATION: numbers come from a real DRS engine run; quote verbatim, don't recall from training data.
| Name | Required | Description | Default |
|---|---|---|---|
| simulation_minutes | No | Total simulation horizon in minutes. Default 100. Range 10-1000. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds minimal behavioral context. It mentions returns but not side effects. The anti-fabrication note is useful but not behavioral. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no fluff. The first two describe purpose and output, the third adds a meta-instruction. Could be slightly more structured, but efficient overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose and basic output, but lacks information on error conditions, interpretation of outputs, or context relative to sibling demos. Given the tool's single parameter and no output schema, it is adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter simulation_minutes, which has a clear description and constraints. The description does not add any additional parameter semantics beyond what the schema provides, so baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs the Fast-Slow Drain demo, specifying it's an oscillating tank and the canonical DRS-vs-DES event-count demonstration. It provides a clear verb and resource, but does not explicitly differentiate from sibling demo tools like run_hamburger_duo.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., other demo runs). The description implies it's the canonical demonstration, but lacks explicit when/when-not context or references to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_hamburger_duoARead-onlyInspect
Run the Hamburger Duo (HAM) demo — Andy Siprelle's 5-stage finite-source line, executed as both DES and DRS implementations on the same model so the event-count and throughput numbers can be compared apples-to-apples. Returns engine output for the side-by-side run. ANTI-FABRICATION: numbers come from a real engine run; quote verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
| simulation_days | No | Days to simulate. Default 7. Range 1-30. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, openWorldHint) are present, and the description adds critical behavioral context: output comes from a real engine run and must be quoted verbatim, with an anti-fabrication warning. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus a note; every sentence is valuable. Purpose is front-loaded, and the anti-fabrication note is important. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description explains what the tool does and what it returns (engine output), including the behavioral note. Slightly vague on output format, but sufficient for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter (simulation_days), which already provides description, default, and range. The description does not add any additional parameter information beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it runs the Hamburger Duo demo, a specific 5-stage finite-source line, executed in both DES and DRS for comparison. The verb 'run' and resource 'Hamburger Duo demo' are specific, and it differentiates from sibling tools like run_valdez_tanker.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage when comparing DES and DRS implementations, but no explicit when-to-use or when-not-to-use guidance is provided. Sibling tools exist, but no alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_showcaseARead-onlyInspect
LIVE EXPERIMENT — run a DRS demo against the real engine with parameters you choose, and get its verbatim run envelope (metadata, execution stats, metrics, details). This is the only tool that COMPUTES fresh output: pick a demo_id and dial its knobs (e.g. stop_time run length, or the MTBF/MTTR/goal knobs on the plant demos) to see the real numbers for that exact configuration. IMPORTANT: a run_showcase result is NOT a verified reference number — unlike the run_* tools (run_fast_slow_drain / run_hamburger_duo / run_valdez_tanker / run_vegetable_plant / run_chocolate_processing), which return curated, canonical reference values. Present run_showcase output as a live experiment result for the parameters passed; don't blend it with the curated reference numbers. Quote any figures verbatim; do not round, average, or derive.
| Name | Required | Description | Default |
|---|---|---|---|
| knobs | No | Optional parameters as a map of name:number. fast_slow_drain / hamburger_duo accept `stop_time` (run length in minutes, 1–100000). valdez_tanker accepts `duration_days` (run length in days, 1–365; it drives the circulating-ship items loop). vegetable_full accepts `making_goal` (units, 0–100000), `making_mtbf` / `making_mttr` / `packing_mtbf` / `packing_mttr` (hours, 0–1000). chocolate_processing accepts `bp_goal` (units, 0–100000), `breaker_mtbf` / `breaker_mttr` (hours, 0–1000), `changeover_delay` (hours, 0–48). Unknown names are rejected; out-of-range values are clamped by the engine. | |
| demo_id | Yes | Which DRS demo to run live against the real engine. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that output is a live experiment result, not a verified reference. Warns about unknown knobs being rejected and out-of-range values being clamped. Annotations (readOnlyHint: true) are compatible with reading fresh output without mutation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is thorough and front-loaded with key purpose and warnings. While slightly long, every sentence adds value, making it well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Fully covers the tool's behavior given its complexity (5 demo variants, parameter mapping, output nature). No output schema is needed as 'verbatim run envelope' sets clear expectations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds significant value beyond the schema by detailing which knobs each demo_id accepts, including ranges and units (minutes, days, units, hours). Schema coverage is 100%, but the description enriches understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs a live DRS demo with user-chosen parameters and returns the verbatim run envelope. It distinguishes itself from sibling run_* tools that return curated reference values.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly explains when to use this tool (to compute fresh output) vs alternatives (curated references by run_* tools). Warns not to blend results and to quote figures verbatim.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_sku_capacityARead-onlyInspect
Run the Bottling Line / SKU-capacity (SKU) demo — a sim-foundation parameter-set example. One 5-machine bottling line run for several products (SKUs as parameter sets). Returns, per SKU, OEE (identical ~55% — time-based interrupts) and indexed real output (swings >3x: 100 / 50 / 30 / 42) plus the pacing machine. Shows you can't read per-SKU capacity off OEE. ANTI-FABRICATION: numbers come from a real sim-foundation engine run (indexed/anonymized); quote verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive behavior. The description adds valuable context: that results come from a real sim-foundation engine, explaining the source of numbers. However, it does not detail potential side effects or authorization needs beyond what annotations state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loaded with the tool's core purpose. It efficiently conveys the demo's mechanics and anti-fabrication note, though the mention of time-based interrupts and output swings could be slightly more compact.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains what the tool returns and the demo's key insight. It covers the tool's function and result semantics, but could benefit from specifying the exact output format or structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the description carries full weight in explaining tool behavior. It thoroughly describes the demo's purpose, inputs (none), and outputs (OEE, output, pacing machine), exceeding the baseline for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs a Bottling Line SKU-capacity demo, specifying it involves a 5-machine line, multiple SKUs, and returns OEE and real output. This differentiates it from sibling demos like run_chocolate_processing or run_tissue_line.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide guidance on when to use this tool over its siblings. It lacks context about the scenarios suited for this specific demo or when to choose alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_tissue_lineARead-onlyInspect
Run the Tissue Line (TIS) demo — a sim-foundation parameter-set example. One tissue line (Reel supply → Converter → Winder), three strategic decisions (each a parameter set): bypass converter / run converter / add storage tower. Returns per-decision throughput as % of nameplate (75.4 / 75.4 / 73.4), the binding bottleneck (the upstream parent-reel supply in all three), and converter/storage detail. Shows the downstream decision barely moves throughput — invest at the constraint. ANTI-FABRICATION: numbers come from a real sim-foundation engine run (indexed/anonymized); quote verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, openWorldHint), the description adds critical behavioral details: the simulation produces real engine-run numbers that must be quoted verbatim, and explains internal logic (bottleneck analysis). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is clear and front-loaded, but slightly verbose. Each sentence adds value, including the anti-fabrication note.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameter-less demo tool without output schema, the description fully covers what the tool does, what it returns (specific throughput percentages and bottleneck), and special behavior (verbatim quoting).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so baseline is 4. The description compensates with rich output information, making parameter explanation unnecessary.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs a Tissue Line demo simulation with specific strategic decisions and output details, distinguishing it from sibling simulation tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage as a demo for understanding bottleneck constraints, but does not explicitly compare to sibling tools such as run_vegetable_plant or run_chocolate_processing, nor provides when-to-use or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_valdez_tankerBRead-onlyInspect
Run the Valdez Tanker (VALD) demo — Koelling-Remy 1983 Alaska Pipeline model, the paradigm-integration motivator. Crude flows continuously into the Valdez Marine Terminal storage tank (Flow); tankers arrive discretely to drain it (Item); DRS handles both via F2I / I2F transitions. Returns engine output including tanker arrival/departure events and tank-level trace. ANTI-FABRICATION: numbers come from a real engine run; quote verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
| simulation_days | No | Days to simulate. Default 30. Range 1-90. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds that output includes 'tanker arrival/departure events and tank-level trace' and includes an anti-fabrication warning. However, it does not disclose rate limits, authorization needs, or other behavioral traits beyond what is already in annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise, with four sentences front-loading the purpose. The anti-fabrication note is relevant but slightly extraneous. Still, every sentence adds value, and there is no wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter, no output schema, and rich annotations, the description adequately explains what the demo does and what output to expect. However, it lacks detail on how to interpret the return or any prerequisites, making it adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a clear parameter (simulation_days) including default, min, max, and description. The tool description adds no additional meaning beyond what the schema provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action ('Run'), the specific demo ('Valdez Tanker'), and provides context about the model (Koelling-Remy 1983, Alaska Pipeline, paradigm-integration motivator). Distinguishes from sibling tools like 'run_fast_slow_drain' and 'run_hamburger_duo' by naming a unique demo.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Does not provide guidance on when to use this tool versus alternatives. No mention of prerequisites, scenarios, or exclusions. The description only explains what the tool does, not when an agent should choose it over sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_vegetable_plantARead-onlyInspect
Run the Vegetable Plant (VEG) demo — a Plant Builder distribution-control model. Two Making lines feed five Packing lines through eight surge bins; a DRS rate solver splits and rebalances the flow across the bins as the plant works through its campaign schedule. Returns the plant rollup (utilization, campaigns, active window), per-product goal attainment, per-system campaign timelines, and final surge-bin / delivered levels. ANTI-FABRICATION: numbers come from a real Plant Builder engine run; quote verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds context about the internal model and the anti-fabrication note, which is valuable beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the purpose. Every sentence adds value, including the anti-fabrication note and the output summary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description thoroughly explains what the tool returns (rollup, goal attainment, timelines, levels). It is complete and provides sufficient context for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so schema coverage is 100%. The description does not need to add parameter information, and baseline 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs the Vegetable Plant demo, a specific distribution-control model. It names the demo and describes the system, but does not explicitly distinguish from sibling tools like run_chocolate_processing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for this specific demo, but provides no explicit guidance on when to use it versus alternative run_* tools. No when-not or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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